We utilize the new semantic frame parsing pipeline in our twitter parsing web API, as shown in Figure REF . This web portal is designed for parsing tweets. After user types in the tweets, then the system can both extract the tagged slots and identify the OOV and OOD patterns. The system also allow users to submit their own dataset and retrain their models based on the new submitted dataset. All the user submitted datasets, the trained corresponding parsing models and the training codes are stored on our Azure cloud server [1]}, the web service framework is written in Flask [2]}, and the web server gateway interface HTTP server is based on Gunicorn [3]}.
